Determining Cluster-cluster Aggregation Rate Kernels Using Inverse Methods
Supervisors: Dr. Colm Connaughton and Prof. R. C. Ball.
Funded by: The EPSRC
Brief Description: It would be useful to determine the extent to which the existence of noise, spatial correlations, and other phenomena in a real aggregation process can be `folded back’ into an estimation of a kernel function (obtained using inverse methods) that could then be used within a mean-field phenomenological surrogate. Such a surrogate model might then prove useful for enhancing the accuracy of some models of larger processes that include aggregation processes that are presently weakly modelled, e.g. rain formation in turbulent air within clouds. It might also allow us to deduce key properties of the real aggregation processes in question.
In an initial phase, we seek sufficiently robust, accurate inversion methods for determining kernel functions from benchmark data with known characteristics.
Peter P. Jones, Robin C. Ball, Colm Connaughton, Nonlinear Least Squares Method for the Inverse Droplet Coagulation Problem, Phys. Rev. E, 88, 012138, (July 2013)
Robin C. Ball, Colm Connaughton, Peter P. Jones, R. Rajesh, Oleg Zaboronski, Collective Oscillations in Irreversible Coagulation Driven by Monomer Inputs and Large-Cluster Outputs, Phys. Rev. Lett. 109, 168304 (2012)
Colm Connaughton and Peter P. Jones, Some Remarks on the Inverse Smoluchowski Problem for Cluster-Cluster Aggregation
J. Phys.: Conf. Ser. 333 012005 (2011), doi:10.1088/1742-6596/333/1/012005
MA, Analytic Philosophy (Univ. of Nottingham, 1997)
MSc, Complexity Science (Univ. of Warwick, 2009)
PhD, Theoretical Physics and Complexity Science (Univ. of Warwick, 2013). [Thesis document.]
Produced a slideshow for the European Conference on Complex Systems 2009 public session.
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Licence: For research, educational, and non-commercial use.